A TEST OF SEVERAL EVAPORATION EQUATIONS FOR WATER TEMPERATURE SIMULATIONS IN LAKES1

ABSTRACT: Evaporative heat loss is an essential component of any heat budget used for the modeling of lake water temperatures. Seven evaporative heat loss equations were tested in a year-round, physically-based temperature and dissolved oxygen model for lakes. Deciding which equation to choose for use in the year-round model was based on the goodness of fit of the simulated vs. measured surface temperatures, which were taken at a depth of 1 m below the water surface. An equation which includes free and forced convection components and which was previously used for cooling ponds gave the best fit between temperature simulations and measurements.     

AIR-WATER TEMPERATURE RELATIONSHIP IN ILLINOIS RIVER1

Application of harmonic analysis to daily mean air, water temperature records for a location indicates that the first harmonic accounts for a major portion of the total variance in the records. Water temperature residuals are well correlated with air temperature residuals. Parametric values of the mathematical model for predicting water temperatures from air temperature records are stable from year to year. The air-water temperature relationship appears to be a stationary linear process. Consequently, it is possible to predict water temperatures at a location from the ambient temperature records provided both air and water temperature records are available for another similarly situated water body.     

A RATING-CURVE METHOD FOR HYDRODYNAMIC SIMULATIONS IN CONTAMINANT TRANSPORT MODELING1

ABSTRACT: Accurate prediction of hydrodynamics is of great importance to modeling contaminant transport and water quality in a river. Flow conditions are needed in estimating potential exposure contamination levels and the recovery time for a no-action alternative in contaminated sediments remediation. Considering highly meandering characteristics of the Buffalo River, New York, a three-dimensional hydrodynamic model was selected to route upstream flows through the 8-km river section with limited existing information based on the model's fully predictive capability and process-oriented feature. The model was employed to simulate changes in water depth and flow velocity with space and time in response to variation in flow rate and/or water surface elevation at boundaries for given bottom morphometry and initial conditions. Flow conditions of the river reach where historical flow data are not available were computed. A rating-curve approach was developed to meet continuous and event contaminant modeling needs. Rating curves (depth-discharge and velocity-discharge relationships) were constructed at selected stations from the 3-D hydrodynamic simulations of individual flow events. The curves were obtained as steady solutions to an unsteady problem. The rating-curve approach serves to link flow information provided by the hydrodynamic model to a contaminant transport model. With the approach, the linking problem resulting from incompatible model dimensions and grid sizes can be solved. The curves will be used to simulate sediment movement and to predict contaminant fate and transport in the river.     

System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts

Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.     

INDEXED SEQUENTIAL HYDROLOGIC MODELING FOR HYDROPOWER CAPACITY ESTIMATION1

ABSTRACT: The indexed sequential hydrologic modeling (ISM) methodology is utilized by the Western Area Power Administration as the basis for risk-based estimation of project-dependable hydropower capacity for several federally owned/operated projects. ISM is a technique based on synthetic generation of a series of overlapping short-term inflow sequences obtained directly from the historical record. The validity of ISM is assessed through application to the complex multireservoir hydropower system of the Colorado River basin for providing risk estimates associated with determination of reliable hydrogeneration capacity. Performance of ISM is compared with results from stochastically generated streamflow input data to the Colorado River Simulation System (CRSS). Statistical analysis and comparison of results are based on monthly power capacity, energy generation, and downstream water deliveries. Results indicate that outputs generated from ISM synthetically generated sequences display an acceptable correspondence with those obtained from stochastically generated hydrologic data for the Colorado River Basin.     

EFFECTS OF CLIMATE CHANGE ON HYDROLOGY AND WATER RESOURCES IN THE COLUMBIA RIVER BASIN1

ABSTRACT: As part of the National Assessment of Climate Change, the implications of future climate predictions derived from four global climate models (GCMs) were used to evaluate possible future changes to Pacific Northwest climate, the surface water response of the Columbia River basin, and the ability of the Columbia River reservoir system to meet regional water resources objectives. Two representative GCM simulations from the Hadley Centre (HC) and Max Planck Institute (MPI) were selected from a group of GCM simulations made available via the National Assessment for climate change. From these simulations, quasi-stationary, decadal mean temperature and precipitation changes were used to perturb historical records of precipitation and temperature data to create inferred conditions for 2025, 2045, and 2095. These perturbed records, which represent future climate in the experiments, were used to drive a macro-scale hydrology model of the Columbia River at 1/8 degree resolution. The altered streamflows simulated for each scenario were, in turn, used to drive a reservoir model, from which the ability of the system to meet water resources objectives was determined relative to a simulated hydrologic base case (current climate). Although the two GCM simulations showed somewhat different seasonal patterns for temperature change, in general the simulations show reasonably consistent basin average increases in temperature of about 1.8–2.1°C for 2025, and about 2.3–2.9°C for 2045. The HC simulations predict an annual average temperature increase of about 4.5°C for 2095. Changes in basin averaged winter precipitation range from -1 percent to + 20 percent for the HC and MPI scenarios, and summer precipitation is also variously affected. These changes in climate result in significant increases in winter runoff volumes due to increased winter precipitation and warmer winter temperatures, with resulting reductions in snowpack. Average March 1 basin average snow water equivalents are 75 to 85 percent of the base case for 2025, and 55 to 65 percent of the base case by 2045. By 2045 the reduced snowpack and earlier snow melt, coupled with higher evapotranspiration in early summer, would lead to earlier spring peak flows and reduced runoff volumes from April-September ranging from about 75 percent to 90 percent of the base case. Annual runoff volumes range from 85 percent to 110 percent of the base case in the simulations for 2045. These changes in streamflow create increased competition for water during the spring, summer, and early fall between non-firm energy production, irrigation, instream flow, and recreation. Flood control effectiveness is moderately reduced for most of the scenarios examined, and desirable navigation conditions on the Snake are generally enhanced or unchanged. Current levels of winter-dominated firm energy production are only significantly impacted for the MPI 2045 simulations.     

A Method to Consider Whether Dams Mitigate Climate Change Effects on Stream Temperatures

This article provides a method for examining mesoscale water quality objectives downstream of dams with anticipated climate change using a multimodel approach. Coldwater habitat for species such as trout and salmon has been reduced by water regulation, dam building, and land use change that alter stream temperatures. Climate change is an additional threat. Changing hydroclimatic conditions will likely impact water temperatures below dams and affect downstream ecology. We model reservoir thermal dynamics and release operations (assuming that operations remain unchanged through time) of hypothetical reservoirs of different sizes, elevations, and latitudes with climate‐forced inflow hydrologies to examine the potential to manage water temperatures for coldwater habitat. All models are one dimensional and operate on a weekly timestep. Results are presented as water temperature change from the historical time period and indicate that reservoirs release water that is cooler than upstream conditions, although the absolute temperatures of reaches below dams warm with climate change. Stream temperatures are sensitive to changes in reservoir volume, elevation, and latitude. Our approach is presented as a proof of concept study to evaluate reservoir regulation effects on stream temperatures and coldwater habitat with climate change.     

GENERATING STREAMFLOW SEQUENCES WITH TREND AND CYCLICAL MOVEMENTS1

ABSTRACT: A first-order autoregressive model had been modified with the trend and cyclical movements to generate the streamflow sequences. Three main portions were involved in this modified model, i.e., six-year cycle with trend changing; six-year cycle without trend changing; and both annual and six-year cycles with trend changing. The synthetic sequences of monthly streamflow sequences were compared with the historical records obtained from the Kissimmee River basin by using the Chi-Square test for goodness-of-fit. The results indicated that the newly proposed model has a better solution than the original model because the trend and cyclical movements involved in generating sequences are much closer to the historical records.     

Sensitivity of Thermal Habitat of a Trout Stream to Potential Climate Change,Wisconsin, United States1

Abstract: Airborne thermal remote sensing from four flights on a single day from a single‐engine airplane was used to collect thermal infrared data of a 10.47‐km reach of the upper East Branch Pecatonica River in southwest Wisconsin. The study uses a one‐dimensional stream temperature model calibrated with the longitudinal profiles of stream temperature created from the four thermal imaging flights and validated with three days of continuous stream temperature data from instream data loggers on the days surrounding the thermal remote‐sensing campaign. Model simulations were used to quantify the sensitivity of stream thermal habitat to increases in air and groundwater temperature and changes in base flow. The simulations indicate that stream temperatures may reach critical maximum thresholds for brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) mortality, particularly if both air temperature increases and base flow declines. The approach demonstrates that thermal infrared data can greatly assist stream temperature model validation due to its high spatial resolution, and that this spatially continuous stream temperature data can be used to pinpoint spatial heterogeneity in groundwater inflow to streams. With this spatially distributed data on thermal heterogeneity and base‐flow accretion, stream temperature models considering various climate change scenarios are able to identify thermal refugia that will be critical for fisheries management under a changing climate.     

MODELING AND CONTROL OF THE POLLUTION OF WATER RESOURCES SYSTEMS VIA MULTILEVEL APPROACH1

ABSTRACT. The problem of modeling and control of water pollution is considered. A general mathematical model, where the pollution effluent is discharged directly into the river, into the lake, or into a bypass pipe leading to an advanced Waste Water Treatment (AWT) plant, is developed. The Water Resource System (WRS) under consideration is decomposed into N subsystems. The pollution effluent input vector to each subsystem includes the water quantity and different water characteristics such as BOD, DO, pH, conductivity, temperature, algae, phosphates, nitrates, etc. Treatment cost functions and quality transition functions as well as system model constraints are introduced, where all functions can be nonlinear. A system Lagrangian is formed to incorporate the system constraints and coupling. The Lagrangian is decomposed into N independent subsystems, and a two level optimization methodology is introduced. Each subsystem is independently and separately minimized at the first level assuming known Lagrange multipliers. At the second level, the total Lagrangian is maximized with respect to the Lagrange multipliers using optimal values for effluent inputs from all subsystems obtained from the first level. Economic interpretation on the Lagrange multipliers reveals that they are merely prices imposed by the central authority (second level) for the pollution caused by the subsystems. Advantages of the multilevel approach are discussed.     

APPLICATION OF A LOW-FLOW ASSESSMENT MODEL FOR THE MONONGAHELA RIVER BASIN1

ABSTRACT: The application of a low-flow assessment model is illustrated for the Monogahela River Basin. The model simulates the impact of reservoir operating rules and consumptive use limitation policies on low-flow frequency at downstream locations in the basin. Policies are evaluated using an observed flow sequence and synthetic flow inputs. The paper reviews the historical development of flow management on the Monogahela to provide background for the current study.     

Characterizing Uncertainty in Daily Streamflow Estimates at Ungauged Locations for the Massachusetts Sustainable Yield Estimator

Hydrologic characterization at ungauged locations is one of the quintessential challenges of hydrology. Beyond simulation of historical streamflows, it is similarly important to characterize the level of uncertainty in hydrologic estimates. In tandem with updates to Massachusetts Sustainable Yield Estimator, this work explores the application of global uncertainty estimates to daily streamflow simulations. Expanding on a method developed for deterministic modeling, this approach produces confidence intervals on daily streamflow developed through nonlinear spatial interpolation of daily streamflow using flow duration curves; the 95% confidence is examined. Archived cross‐validations of daily streamflows from 66 watersheds in and around Massachusetts are used to evaluate an approach to uncertainty characterization. Neighboring sites are treated as ungauged, producing relative errors that can be resampled and applied to target sites. The method, with some modification, is found to provide appropriately narrow confidence intervals that contain 95% of the observed streamflows in cross‐validation. Further characterizing uncertainty, multiday means of daily streamflow are evaluated. Working through cross‐validation in Massachusetts, two‐ to three‐month averages of daily streamflow show the best performance. These two approaches to uncertainty characterization inform how streamflow simulation produced for prediction in ungauged basins can be used for water resources management.     

Evaluating the impact of water conservation on fate of outdoor water use: a study in an arid region

In this research, the impact of several water conservation policies and return flow credits on the fate of water used outdoors in an arid region is evaluated using system dynamics modeling approach. Return flow credits is a strategy where flow credits are obtained for treated wastewater returned to a water body, allowing for the withdrawal of additional water equal to the amount returned as treated wastewater. In the return credit strategy, treated wastewater becomes a resource. This strategy creates a conundrum in which conservation may lead to an apparent decrease in water supply because less wastewater is generated and returned to water body. The water system of the arid Las Vegas Valley in Nevada, USA is used as basis for the dynamic model. The model explores various conservation scenarios to attain the daily per capita demand target of 752 l by 2035: (i) status quo situation where conservation is not implemented, (ii) conserving water only on the outdoor side, (iii) conserving water 67% outdoor and 33% indoor, (iv) conserving equal water both in the indoor and outdoor use (v) conserving water only on the indoor side. The model is validated on data from 1993 to 2008 and future simulations are carried out up to 2035. The results show that a substantial portion of the water used outdoor either evapo-transpires (ET) or infiltrates to shallow groundwater (SGW). Sensitivity analysis indicated that seepage to groundwater is more susceptible to ET compared to any other variable. The all outdoor conservation scenario resulted in the highest return flow credits and the least ET and SGW. A major contribution of this paper is in addressing the water management issues that arise when wastewater is considered as a resource and developing appropriate conservation policies in this backdrop. The results obtained can be a guide in developing outdoor water conservation policies in arid regions.     

Fatty Acids as Phase Change Materials (PCMs) for Thermal Energy Storage: A Review

Abstract

Thermal energy storage (TES) technologies in general and phase change materials (PCMs) in particular, have been topic in research for the last 20 years. Traditionally, available heat has been stored in the form of sensible heat (typically by raising temperature of water, rocks, etc). Latent heat storage on the other hand, is a novel and developing technology, which has found considerable interest due to its operational advantages of smaller temperature swing, smaller size, and lower weight per unit of storage capacity. The interest on thermal energy storage by using fatty acids as PCM has risen in recent times since they have desired thermodynamic and kinetic criteria for low temperature latent heat storage. An added advantage is that fatty acids are derived from the vegetable and animals oil that provides an assurance of continuous supply. This article will review the development of fatty acids as PCMs for solar thermal energy storage application.     

A provider-based water planning and management model--WaterSim 4.0--for the Phoenix Metropolitan Area

Uncertainty in future water supplies for the Phoenix Metropolitan Area (Phoenix) are exacerbated by the near certainty of increased, future water demands; water demand may increase eightfold or more by 2030 for some communities. We developed a provider-based water management and planning model for Phoenix termed WaterSim 4.0. The model combines a FORTRAN library with Microsoft C# to simulate the spatial and temporal dynamics of current and projected future water supply and demand as influenced by population demographics, climatic uncertainty, and groundwater availability. This paper describes model development and rationale. Water providers receive surface water, groundwater, or both depending on their portfolio. Runoff from two riverine systems supplies surface water to Phoenix while three alluvial layers that underlie the area provide groundwater. Water demand was estimated using two approaches. One approach used residential density, population projections, water duties, and acreage. A second approach used per capita water consumption and separate population growth estimates. Simulated estimates of initial groundwater for each provider were obtained as outputs from the Arizona Department of Water Resources (ADWR) Salt River Valley groundwater flow model (GFM). We compared simulated estimates of water storage with empirical estimates for modeled reservoirs as a test of model performance. In simulations we modified runoff by 80%-110% of the historical estimates, in 5% intervals, to examine provider-specific responses to altered surface water availability for 33 large water providers over a 25-year period (2010-2035). Two metrics were used to differentiate their response: (1) we examined groundwater reliance (GWR; that proportion of a providers' portfolio dependent upon groundwater) from the runoff sensitivity analysis, and (2) we used 100% of the historical runoff simulations to examine the cumulative groundwater withdrawals for each provider. Four groups of water providers were identified, and discussed. Water portfolios most reliant on Colorado River water may be most sensitive to potential reductions in surface water supplies. Groundwater depletions were greatest for communities who were either 100% dependent upon groundwater (urban periphery), or nearly so, coupled with high water demand projections. On-going model development includes linking WaterSim 4.0 to the GFM in order to more precisely model provider-specific estimates of groundwater, and provider-based policy options that will enable "what-if" scenarios to examine policy trade-offs and long-term sustainability of water portfolios.     

AN INTERACTWE MODELING ENVIRONMENT FOR NON-POINT SOURCE POLLUTION CONTROL1

ABSTRACT: Non-point source pollution cuntinues to be an important environmental and water quality management problem. For the moat part, analysis of non-point source pollution in watersheds has depended on the use of distributed models to identify potential problem areas and to assess the effectiveness of alternative management practices. To effectively use these models for watershed water quality management, users depend on integrated geographic information systems (GIS)-based interfaces for input/output data management. However, existing interfaces are ad-hoc and the utility of GIS is limited to organization of input data and display of output data. A highly interactive water quality modeling interface that utilizes the functional components and analytical capability of GIS is highly desirable. This paper describes the tight coupling of the Agricultural Non-point Source (AGNPS) water quality model and ARC/INFO GIS software to provide an interactive hybrid modeling environment for evaluation of non-point source pollution in a watershed. The modeling environment is designed to generate AGNPS input parameters from user-specified GIS coverages, create AGNPS input data files, control AGNPS model simulations, and extract and organize AGNPS model output data for display. An example application involving the estimation of pesticide loading in a southern Iowa agricultural watershed demonstrates the capability of the modeling environment. Compared with traditional methods of watershed water quality modeling using the AGNPS model or other ad-hoc interfaces between a distributed model and GIS, the interactive modeling environment system is efficient and significantly reduces the task of watershed analysis using tightly coupled GIS databases and distributed models.     

STREAM TEMPERATURE CORRELATIONS WITH AIR TEMPERATURES IN MINNESOTA: IMPLICATIONS FOR CLIMATE WARMING1

ABSTRACT: Air temperatures are sometimes used as easy substitutes for stream temperatures. To examine the errors associated with this substitution, linear relationships between 39 Minnesota stream water temperature records and associated air temperature records were analyzed. From the lumped data set (38,082 daily data pairs), equations were derived for daily, weekly, monthly, and annual mean temperatures. Standard deviations between all measured and predicted water temperatures were 3.5°C (daily), 2.6°C (weekly), 1.9°C (monthly), and 1.3°C (annual). Separate analyses for each stream gaging station gave substantially lower standard deviations. Weather monitoring stations were, on average, 37.5 km from the stream. The measured water temperatures follow the annual air temperature cycle closely. No time lags were taken into account, and periods of ice cover were excluded from the analysis. If atmospheric CO₂ doubles in the future, air temperatures in Minnesota are projected (CCC GCM) to rise by 4.3°C in the warm season (April-October). This would translate into an average 4.1°C stream temperature rise, provided that stream shading would remain unaltered.     

Endosulfan transport: I. Integrative assessment of airborne and waterborne pathways

To reduce endosulfan (C9H6O3Cl6S; 6,7,8,9,10,10-hexachloro-1,5, 5a,6,9,9a-hexahydro-6,9-methano-2,4,3-benzodioxathiepin 3-oxide) contamination in rivers and waterways, it is important to know the relative significances of airborne transport pathways (including spray drift, vapor transport, and dust transport) and waterborne transport pathways (including overland and stream runoff). This work uses an integrated modeling approach to assess the absolute and relative contributions of these pathways to riverine endosulfan concentrations. The modeling framework involves two parts: a set of simple models for each transport pathway, and a model for the physical and chemical processes acting on endosulfan in river water. An averaging process is used to calculate the effects of transport pathways at the regional scale. The results show that spray drift, vapor transport, and runoff are all significant pathways. Dust transport is found to be insignificant. Spray drift and vapor transport both contribute low-level but nearly continuous inputs to the riverine endosulfan load during spraying season in a large cotton (Gossypium hirsutum L.)-growing area, whereas runoff provides occasional but higher inputs. These findings are supported by broad agreement between model predictions and observed typical riverine endosulfan concentrations in two rivers.     

Stream Temperature Modeling Using Functional Regression Models

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.     

A Comparison of Statistical Approaches for Predicting Stream Temperatures Across Heterogeneous Landscapes1

Abstract: Estimating stream temperatures across broad spatial extents is important for regional conservation of running waters. Although statistical models can be useful in this endeavor, little information exists to aid in the selection of a particular statistical approach. Our objective was to compare the accuracy of ordinary least‐squares multiple linear regression, generalized additive modeling, ordinary kriging, and linear mixed modeling (LMM) using July mean stream temperatures in Michigan and Wisconsin. Although LMM using low‐rank thin‐plate smoothing splines to measure the spatial autocorrelation in stream temperatures was the most accurate modeling approach; overall, there were only slight differences in prediction accuracy among the evaluated approaches. This suggests that managers and researchers can select a stream temperature modeling approach that meets their level of expertise without sacrificing substantial amounts of prediction accuracy. The most accurate models for Michigan and Wisconsin had root mean square errors of 2.0‐2.3°C, suggesting that only relatively coarse predictions can be produced from landscape‐based statistical models at regional scales. Explaining substantially more variability in stream temperatures likely will require the collection of finer‐scale hydrologic and physiographic data, which may be cost prohibitive for monitoring and assessing stream temperatures at regional scales.